Numerical Failure Analysis of Cut and Cover Tunnel Against Surface Blast
Bibliographic record
Abstract
The cut-and-cover approach is widely utilized in the construction of shallow utility and transportation tunnels among many tunneling methods.Metro cities and their suburbs globally feature both bottom-up and top-down cut-and-cover tunnels.This work employs numerical analysis to evaluate a cut-and-cover tunnel situated beneath an active roadway in response to accidental surface blast loads, utilizing the finite element approach.The adjacent soil has been represented using the Mohr-Coulomb Plasticity (MC) model.The Concrete Damage Plasticity (CDP) model accounts for concrete behavior, whereas the Johnson-Cook (JC) model represents the elastoplastic behavior of steel reinforcement.The US Army's CONWEP (Conventional Weapons) model integrates the explosion effects of trinitrotoluene (TNT) explosive material on the soil tunnel model.This numerical analysis was performed on sandy clay soil, with the TNT weight deemed similar to that of a small delivery truck's capacity i.e. (1814 kg).The soil cover above the underground structure has been adjusted based on the d/H ratio (where d represents the depth of the soil cover and H denotes the height of the tunnel cross-section).Ultimately, a mitigation analysis has been conducted by substituting the concrete with an energy-absorbing material, steel-fiberreinforced concrete (SFRC), for the tunnel liner.SFRC substantially mitigates tensile damage in the concrete liner, hence improving tunnel safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".